Extended clustering for improved positioning

ABSTRACT

A method for clustering position determinations is used for providing position determination assisting data in a cellular communications network. The method comprises obtaining ( 209 ) a main cluster of points, which are results of high-precision position determinations. The method further comprises separating out ( 220 ) of at least two subclusters. The points of the subclusters have a local density of points which is above a predetermined local density threshold. A method for providing position determination assisting data in a cellular communications network comprising the clustering of position determinations is also presented as well as an arrangement for providing position determination assisting data in a cellular communications network, a node of a cellular communications network, a cellular communications network and a computer readable medium comprising position determination assisting data are disclosed.

TECHNICAL FIELD

The present invention relates in general to methods and systems forposition determination of mobile terminals in a cellular communicationsnetwork, and in particular to such position determination involving cellareas.

BACKGROUND

All cellular communications systems are divided into cells, where UserEquipment (UE) served by one, or when in soft(er) handover several basestations. Each base station may serve UEs in more than one cell. Theimportant point from a positioning and navigation perspective is thatthe cell where a specific UE is located is known in the cellular system.Hence, after determination of the geographical area covered by aspecific cell, it can be stated that the UE is located somewhere withinsaid geographical area, as long as it is connected and the reported cellidentity of the serving cell is equal to the cell identity correspondingto the particular geographical area.

An example of positioning within a Wideband Code Division MultipleAccess (WCDMA) cellular system operates briefly as follows, assumingthat the positioning operates over the Radio Access Network ApplicationPart (RANAP) interface. The procedures are however similar for e.g. theGlobal System for Mobile communications (GSM) and Code Division MultipleAccess 2000 (CDMA 2000).

A message requesting a location estimate is received in the ServingRadio Network Controller (SRNC) over the RANAP interface. The quality ofservice parameters of the message is assumed to be such that the RadioNetwork Controller (RNC) selects the cell identity positioning method.The SRNC determines the serving cell identity of the UE to be positionedand retrieves a pre-stored polygon that represents the extension of theserving cell. The SRNC sends the resulting cell polygon back to the corenetwork over the RANAP interface, using a cell polygon format in alocation report message.

It should, however, be noted that due to the complexity of the radiopropagation, the cell polygon format is only an approximation of theextension of the true cell. The selection of the polygon format isdictated by the need to have a reasonably flexible geographicalrepresentation format, taking e.g. computation complexities andreporting bandwidths into account.

Since the polygon format approximates the cell extension, the polygon isnormally pre-determined in a cell-planning tool to represent the cellextension with a certain confidence. The confidence is intended torepresent the probability that the UE is located within the polygon,conditioned on the fact that it is connected to the cell that isrepresented by the cell polygon. The underlying off-line calculation ofthe cell polygon can e.g. be based on coverage simulations of varyinglevels of sophistication. However, the end result is normally not veryreliable when the confidence of the calculated cell extension isconsidered.

A particular difficult task is cell-ID positioning in cells having acomplex distribution pattern for the probability of locations of UEs.The defined cell extension will typically also include areas where theprobability to find a UE is very low. This obviously decreases theoverall level of positioning accuracy.

The accuracy of the cell identity positioning method is mainly limitedby the size of the cell, something that prevents it from being used inmore sophisticated navigation applications. Its main advantages includea very low response time as well as the fact that it is widely spreadand always available where there is cellular coverage. The cell identitymethod is also straightforward to implement and has no UE impact. Theadvantages has lead to an interest for the development of Enhanced cellidentity (E-cell ID) positioning methods that aim at enhancing theaccuracy of the basic cell identity method at the same time as theadvantages of the method are retained.

One principle for E-cell ID positioning aims at combining the cellextension model with a distance measure. Two possibilities towards thisend are Round Trip Time (RTT) measurements and path loss measurements.The most accurate of these two alternatives is the RTT measurement. Thepath loss measurement suffers from shadow fading effects. which resultin accuracies that are of the order of half the distance to the UE. Inthe RTT measurement principle, the travel time of radio waves from theRadio Base Station (RBS) to the UE and back is measured. The RTT methodalone defines a circle around the RBS. By combining this informationwith the cell polygon, left and right angles of the circle can becomputed.

Another idea for enhanced cell identity positioning has been to usepre-calculated maps of the regions where the UE is in soft(er) handoverwith one or several cells. Such areas are significantly smaller than thewhole cell opening up for a better accuracy of the determined position.Normally these maps are pre-calculated in the planning tool, exactly asthe ordinary cell polygons.

In some situations high-precision positioning is required. In thepresent disclosure, “high-precision positioning methods” are defined todenote positioning methods that have a potential to meet theNorth-American E-911 emergency positioning requirements. Methods thatmeet these requirements are capable of obtaining positioning accuraciesof:

-   -   either (terminal based) 50 meters (67%) and 150 m (95%),    -   or (network based) 100 meters (67%) and 300 m (95%).

Assisted Global Positioning System (A-GPS) positioning is an enhancementof the Global Positioning System (GPS). GPS reference receivers attachedto e.g. a cellular communication system collect assistance data that,when transmitted to GPS receivers in terminals connected to the cellularcommunication system, enhance the performance of the GPS terminalreceivers. Typically, A-GPS accuracy can become as good as 10 meters.Additional assistance data is collected from the cellular communicationsystem directly, typically to obtain a rough initial estimate of theposition of the terminal together with a corresponding uncertainty ofthe initial estimate. This position is often given by a cell identitypositioning step.

The Uplink Time Difference Of Arrival (UTDOA) positioning method isbased on time of arrival measurements performed in several RBSs oftransmissions from the UEs. The signal strengths are higher than inA-GPS, something that enhances the ability to perform positioningindoors. The accuracy of UTDOA is expected to be somewhat worse thanthat of A-GPS though, mainly because the radio propagation conditionsare worse along the surface of the earth than when GPS radio signals arereceived from satellites at high elevation angles.

A general problem with existing positioning methods based on cell-ID isthat the accuracy of the determined positions is low, in particular forcells having complex shapes for UE positioning. The confidence value isnormally not determined with the best possible accuracy, with respect tothe calculated cell area.

SUMMARY

A general object of the present invention is thus to provide formethods, devices and systems giving possibilities for improved positiondetermination accuracy. A further object is to provide for methods anddevices providing positioning assisting data allowing for positiondeterminations of a higher accuracy, in particular for cells havingcomplex shapes for UE positioning. Yet a further object of the presentinvention is to provide for methods, devices and systems operating withsmaller distinguishable areas. It is also a further object of thepresent invention is to provide for methods, devices and systems whichprovides defined areas having a well established confidence value.

The above objects are achieved by methods, devices and systems accordingto the enclosed patent claims. In general words, in a first aspect, amethod for clustering position determinations is used for providingposition determination assisting data in a cellular communicationsnetwork. The method comprises obtaining a main cluster of points, whichare results of high-precision position determinations. The methodfurther comprises separating out of at least two subclusters. The pointsof the subclusters have a local density of points which is above apredetermined local density threshold.

In a second aspect, a method for providing position determinationassisting data in a cellular communications network comprisesestablishing of a cell relation configuration for a user equipment. Thecell relation configuration comprises at least cell identities of cells,in which signals to and/or from the user equipment fulfill at least aspecific radio condition criterion when received. The method furthercomprises performing of a high-precision position determination for theuser equipment. The establishing and performing steps are repeated aplurality of times. The method further comprises clustering of pointsbeing results of the high-precision position determinations belonging tothe same cell relation configuration in separate main clusters of pointsand splitting at least one of the separate main clusters of points insubclusters according to the first aspect. The method also comprisesassociating of an area definition with at least one of the main clustersof points and creating of position determination assisting datacomprising a relation between the cell relation configurations and theassociated area definitions.

In a third aspect, an arrangement for providing position determinationassisting data in a cellular communications network comprises means forestablishing a cell relation configuration for a user equipment. Thecell relation configuration comprises at least cell identities of cells,in which signals to and/or from the user equipment fulfill at least aspecific radio condition criterion when received. The arrangementfurther comprises means for performing a high-precision positiondetermination for the user equipment and means for clustering results ofthe high-precision position determinations belonging to the same cellrelation configuration in separate main clusters of points. The meansfor clustering results is further arranged for separating out, from atleast one of the separate main clusters, at least two subclusters. Thepoints of the subclusters have a local density of points above apredetermined local density threshold. The arrangement further comprisesmeans for associating an area definition with at least two subclustersof the main cluster of points and creating position determinationassisting data comprising a relation between the cell relationconfigurations and the associated area definitions.

In a fourth aspect, a node of a cellular communications networkcomprises an arrangement according to the third aspect.

In a fifth aspect, a cellular communications network comprises anarrangement according to the third aspect.

In a sixth aspect, a computer readable medium comprises positiondetermination assisting data provided by a method according to the firstor second aspect.

Among the numerous advantages of the present invention can be mentionedthe following: A database of area definitions for cell relationconfigurations are built up adaptively and automatically. The splittingof clusters into subclusters increases the obtainable positioningaccuracy. The performance of the UTDOA and A-GPS positioning methods canbe improved by initial positioning data obtained by the presentinvention. The area definition information is automatically refined, afact that is useful e.g. when parts of the Radio Network (RAN) isre-planned.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by making reference to the following descriptiontaken together with the accompanying drawings, in which:

FIG. 1 is an illustration of a cellular communications system;

FIGS. 2A-E are illustrations of examples of division of a cell intosmaller areas according to coverage from neighbouring cell signals;

FIGS. 3A-C are illustrations of examples of cell relationconfigurations;

FIG. 4 is a flow diagram of steps of an embodiment of a method accordingto the present invention;

FIG. 5 is a flow diagram of steps of an embodiment of a method accordingto the present invention;

FIG. 6 is a flow diagram of the steps of an embodiment of step 230 ofFIG. 5;

FIG. 7 is an illustration of an example of a distribution of points in amain cluster of points;

FIG. 8 is a flow diagram of the steps of another embodiment of step 230of FIG. 5;

FIG. 9 is an illustration of an example of a distribution of points in amain cluster of points, split into subclusters;

FIG. 10 is a flow diagram of steps of an embodiment of step 212 of FIG.4;

FIG. 11 is an example of a cell polygon; and

FIG. 12 is a block diagram of main parts of an embodiment of a nodeaccording to the present invention;

DETAILED DESCRIPTION

In the present disclosure “position determination assisting data” isused to define data that is used in cell-related activities in cellularcommunications system, such as radio network planning or positioningbased on cell-ID. In particular, it may refer to the cell relationconfiguration and related area definitions used in the presentdisclosure. This should not be mistaken for “assistance data”, which inthe present disclosure is used solely in A-GPS discussions.

In the present disclosure, WCDMA systems are used as a model system.However, anyone skilled in the art realizes that the basic principles ofthe present invention are applicable also to other cellularcommunication system, such as GSM. The invention is thus not limited tothe exemplifying embodiments as such.

FIG. 1 illustrates a general WCDMA system 100. Radio base stations 30(RBS) are spread over the coverage area of the system and servesantennas 20, which in this embodiment are sectorized antennas. A cell 15is associated with each sector of the antennas 20, as the area in whichconnection to the communications system preferably is performed throughthat particular sector. The RBSs 30 are connected to a Radio NetworkController (RNC) node 40, which in a typical case comprises apositioning node 45. The UEs 10 and the RNC 40 communicates over theso-called RRC (Radio Resource Control) interface 37 that is transparentto the RBS 30. The RBSs 30 and the RNC 40 are nodes comprised in theUTRAN (Universal Mobile Telecommunication System Radio Access Network)35. The RNC 40 is further connected to the Core Network (CN) 50 of thecommunications system 100 via a RANAP (Radio Access Network ApplicationPart) interface 47.

A user equipment (UE) 10 is situated in the area covered by the cellularcommunications system 100. The user equipment communicates with the ownradio base station 30 through signals 25. However, also signals 26 fromand to neighbouring RBSs 30 may be possible to detect. If theneighbouring signals 26 are strong enough for supporting actualcommunication, the corresponding cell could be included in a so-calledactive set of cells, which participates in soft(er) handover. (By softhandover is meant the case where two different non-colocated RBSs areused, whereas softer handover refers to one RBS with several sectors.) Aspecial case is when the UE is connected to two sectors of the same RBS,i.e. softer handover. However, for the purpose of the present invention,there is no substantial difference between soft and softer handover andboth cases can be handled analogously. The signal 26 may in some casesbe too weak to be included in the active set, but strong enough to allowfor identification of the transmitting RBS. Such signals may e.g. beused for positioning purposes. Finally, neighbouring signals 26 may alsobe too weak to enable any use at all.

When a UE 10 is connected to a certain RBS via certain radio links, theUE 10 is likely to be situated within the associated cell. The cellarea, in WCDMA defined by a polygon that describes the cell extension,is normally not determined with the best possible accuracy. with respectto the true extension of the cell. The approximate cell area istypically determined in connection with cell planning and may notcorrespond perfectly to the real situation. Normally, the actualconfidence level, i.e. the probability that the UE is actually locatedwithin the specific area, of the cell area extension is not specified.Furthermore, radio conditions may also be altered after the cellplanning has been preformed. It would therefore be advantageous to tunethe confidence and the pre-calculated cell polygon for each cell, usingfield data. This can normally not be afforded though. in particularsince the radio conditions may change with time. The present inventiondisclosure reveals a way to obtain such tuning automatically, also forcomplex shapes.

FIG. 2A illustrates a cell 15, with a UE 10 connected. For simplicity inthe coming explanations, the RBS is in this case assumed to be placed atthe centre of the cell, a so-called omni-cell configuration. When the UE10 is connected to the RBS, it can with a certain probability bedetermined to be present within the cell 15.

However, as mentioned briefly above, the UE may also be within radiorange from other RBSs as well. In FIG. 2B, borders 12 of areas withinwhich signals to/from a neighbouring RBS are strong enough to allow forsoft(er) handover are indicated. In this oversimplified model, theborders 12 are drawn as circles, having their centre at a neighbouringRBS. It is easily seen that the borders 12 divide the cell 15 intosmaller areas 11, 11A, 11B, 11Z. In the area 11Z, only signals from theown RBS 30 are useful. However, in e.g. area 11A, signals to/from oneneighbouring RBS are also useful for soft(er) handover purposes and arethus included in the so-called active set of cells. In area 11B, signalsto/from two neighbouring cells are strong enough and the active set thencomprises two neighbouring cells. It can now easily be understood, thatthe content of the active set can be used for positioning purposes. Byconsulting the active set list, it can be determined in which of thepart areas 11, 11A, 11B, 11Z, the UE 10 is likely to be situated.

However, most often, soft(er) handover information is not used forpositioning purposes, probably since it is likely to be difficult tocompute with a sufficient accuracy. According to the present invention,area definitions that describe any soft(er) handover regions are useful.In WCDMA, such area definitions can conveniently be polygon definitions.However, using prior art cell planning principles would normally notprovide area definitions determined with the best possible accuracy,with respect to the true extension of any soft(er) handover regions.Furthermore, the confidence value of any soft(er) handover regions wouldnormally, using prior art methods, not be determined with the bestpossible accuracy, with respect to any calculated soft(er) handoverarea. It would therefore be advantageous to tune the confidence and thepre-calculated cell polygon for each cell, using field data. This cannormally not be afforded though, in particular since the radioconditions may change with time, even more than for the basic cell.However, the present invention reveals a way to obtain such tuningautomatically.

Signals from neighbouring RBSs can be utilized further. As mentionedabove. even if the signals to and from neighbouring RBSs are not strongenough for allowing soft(er) handover, they may still be strong enoughto enable determination of the identity of the transmitting RBS/UE.

Corresponding set of cells is typically referred to as the detected setof cells. Also this information can be used for positioning purposes. InFIG. 2C, the cell 15 is once again illustrated. Now, not only borders 12for soft(er) handover (of which only one is denoted by a referencenumber) are illustrated, but also borders 13 of areas in which theidentity of the transmitting RBS or UE can be obtained in downlink oruplink, respectively, e.g. corresponding to the detected set of cells.The cell 15 is thereby further divided in even smaller part areas 11,11C-G, 11Z. For instance, in area 11E, signals from one neighbouring RBSare, besides the signals from the own RBS, used for soft(er) handover,while signals from another neighbouring RBS only are used foridentifying the transmitting RBS.

If not only the existence of signals of certain strengths areconsidered, but also the relative strengths as compared to othersignals, an even finer division of the original cell can be achieved. InFIG. 2D, the part areas that involves signals from more than oneneighbouring RBS are divided according to which signal that is thestrongest. Areas 11H-K are thereby possible to define.

As mentioned above, the real situation is, however, not so ideal as theexamples of FIGS. 2A-D may indicate. Instead, the borders 12, 13 are noteasily determined and are typically non-circular. FIG. 2E illustrates asituation that could correspond to a real situation. Anyone skilled inthe art then realises that any theoretical pre-determination of theareas 11, 11A-K, 11Z, is impossible in practice.

In the present invention, two types of information are connected to eachother in order to achieve positioning advantages: cell relationconfiguration and high-precision positioning data.

The first type of information is a cell relation configuration. Thiscell relation configuration corresponds to the divisions in the previousexamples of FIG. 2A-E. The cell relation configuration comprises in abasic embodiment data representing the “own” cell as well as anyneighbouring cell, in which the RBS corresponding theretotransmits/receives detectable signals to/from the user equipment inquestion which fulfill a certain criterion. In a typical view. the cellrelation configuration can be considered as a list of cell identitiescorresponding to signals fulfilling a specific radio condition criterionwith respect to a certain UE. FIG. 3A illustrates an embodiment of sucha list. The first row corresponds to the own cell. The cell ID is “ID1”.The UE can in this example also communicate with cells “ID2”, “ID3”,“ID4”, “ID5”. Each combination of cells will in this embodiment define aparticular cell relation configuration.

FIG. 3B illustrates another embodiment of a cell relation configuration.Here, the relative signal strengths are taken into account, and thecells are thereby sorted in strength order. A signal to/from cell “ID3”is thereby stronger than signals to/from e.g. cells “ID5”. This meansthat a cell relation configuration in this embodiment is not onlydependent on which cells that are comprised in the list, but also inwhich order. There may even be a difference in strength order betweenuplink and downlink, which also can be utilised in defining areas.

Also other signal-strength related quantities can be utilised fordefining the cell relation configuration, e.g. path loss andsignal-to-interference ratio.

FIG. 3C illustrates another embodiment of a cell relation configuration.Here, the signal strengths are also classified. It can be seen that cell“ID1” is classified as “the own cell”, and cells “ID3” and “ID5” areclassified to be comprised in the active set of cells, i.e. they areutilised for soft(er) handover purposes. This means that a cell relationconfiguration in this embodiment is not only dependent on which cellsthat are comprised in the list and in which order, but also on theclassification of the cells.

In the view of the above examples, anyone skilled in the art realizesthat a cell relation configuration is easily obtainable for any UE thatis situated within a coverage area of a cellular communications network.

The second type of necessary data is as mentioned further abovehigh-precision positioning data. This can be derived in any possibleway. UTDOA and A-GPS are mentioned earlier in the background, but othermethods can be useful as well. The idea is to collect relations betweenhigh-precision positioning data and cell relation configuration for thecorresponding UE at the positioning instant. This is preferablyperformed by using measurements of opportunity, i.e. high precisionmeasurements that would anyway be performed for some other reason.Alternatively, the measurements could be arranged on purpose. Forinstance, e.g. for the purpose of improved radio network planning,high-precision position measurement devices could be spread over acertain area in a planned manner. Positions are determined as well ascell relation configurations. Another alternative could be to regularlyorder user equipment capable of high-precision positioning to providesuch measurements. For each possible cell relation configuration (i.e.in a simple view set of ordered cell identities), a measurement list isthen setup. All high-precision measurements that are related to aspecific cell relation configuration are then collected in one specificlist of high-precision measurements. In other words, the high-precisionpositioning data are clustered dependent on the prevailing cell relationconfiguration, giving rise to a number of main clusters of points. Thepoints correspond to the positions determined by the high-precisionmeasurement, and the clusters are defined in terms of prevailing cellrelation configuration of the terminals performing the high-precisionmeasurements. The measurements of one such list thus form a main clusterof measurements of points that can be expected to be located in aspecific geographical area. The clustering of results of thehigh-precision position determinations thus gives a number of separatemain clusters of points. When a suitable number of high-precisionpositioning data points are clustered in one of the separate maincluster of points, it is possible to define an area which contains apre-determined fraction of the high-precision positioning data points.It can then be concluded that a UE having a certain cell relationconfiguration is situated within the defined area with a confidencelevel corresponding to the pre-determined fraction.

In other words, a UE that not by itself has any high-precisionpositioning capabilities may utilise previous high-precision positioningof other UEs for achieving an improved accuracy in positiondetermination by association to the prevailing cell relationconfiguration.

It can be noticed that the achieved area definitions can be considerablydifferent than the actual radio coverage. The reason is that areashaving good radio conditions but never hosting any user equipments willtend to be excluded from the determined area. The associated area willinstead be an area based on a combination of radio coverage propertiesand probability for user equipment occurrence.

Due to the connection with the probability for user equipmentoccurrence, the ideal associated areas can become fairly complex. Inparticular, there may e.g. be areas having a high local density ofpoints separated by areas having a very low local density of points. Asingle associated area must then also include areas having a relativelylow local density of points. This in turn leads to the fact that theassociated area becomes larger than necessary. for covering apredetermined fraction of the points. A larger area means a lowerpositioning accuracy. This problem can, however, be solved. According tothe present invention, a main cluster of points is divided intosubclusters, based on a local density of points. Each subcluster isassociated with a subarea. The area associated with the main clusterthen becomes the aggregate of the subareas. In other words. the idealassociated area may be an area composed by two or more separatedsubareas. This is described more in detail further below.

The general ideas of the clustering approach can also be illustrated bya flow diagram of the main steps of an embodiment of a method forproviding position determination assisting data in a cellularcommunications network, illustrated in FIG. 4A. The procedure starts instep 200. The procedure first comes to a section 202 for providingposition determination assisting data. This section starts with a step204, in which a cell relation configuration for a particular UE isdetermined. The signals are typically registered and reported accordingto standard cellular communication system procedures and compiled tocell relation configuration. In step 206, a high-precision positioningof the UE is performed, using any suitable high-precision positioningmethod. In step 208, the high-precision positioning data is clustereddependent on the determined cell relation configuration into separatemain clusters of points. Examples of this are described more in detailfurther below. The separate main clusters of points can in turn bedivided into subclusters. Details of preferred embodiments separatingthe main clusters of points into subclusters are given further below.The steps 204 to 208 are repeated a number of times, as indicated by thearrow 210.

When an appropriate number of measurement points are available for acertain cell relation configuration, the procedure may continue to step212, in which an area is determined, which resembles the spatialdistribution of the high-precision positioning data. Preferably, an areaas small as possible is computed, which still contains a pre-determinedfraction of the high-precision positioning data. Details of preferredembodiments are given further below. A relation between a certain cellrelation configuration and an area definition is thereby achieved. Iffurther data is added by the steps 204-208, the step 212 may also haveto be repeated as indicated by arrow 214. In particular, if the radioconditions are changing, permanently or for a longer period of time, thearea definitions have to be re-calculated and adapted to the newsituation. Each high-precision position measurement is then alsopreferably time stamped in order to make it possible to discardhigh-precision position measurements that are too old, and successivelyperforming new area optimizations.

The step 212 can be performed for one particular cell relationconfiguration, a group of cell relation configurations or all cellrelation configurations as well as for different clustering selectioncriteria.

The lists of measurements are preferably organized hierarchically sothat lists at higher levels can be constructed from lower levels in casethe number of measurements at lower (more detailed) level would beinsufficient for a reliable computation of a cell polygon.

The driving force behind the present invention is not primarilytroublesome problems with existing technology, rather an insight thatperformance e.g. of the Adaptive Enhanced Cell Identity (AECID)algorithm can be further improved by means of the invention disclosed inthe present invention disclosure.

The focus of the invention is on improvements of the state of the artalgorithm for clustering in AECID, i.e. connected to step 208 of FIG. 4.The improvements will also to some extent influence step 212. The basicalgorithm in normal AECID operates as presented in Appendix A.

The algorithm hence generates one separate main cluster of measurements,for each tag, or equivalent in other words, one separate main cluster ofpoints for each cell relation configuration. In situations where thepoints of a main cluster are distributed in such a manner that they aresituated relatively frequently within one well connected area, theprocedure may proceed to the association step.

However, in situations where the majority of the points of a maincluster are located to several distinct locations, further improvementsin terms of accuracy are possible. according to the present invention.An important feature is to allow generation of multiple smallerclusters, or subclusters, form one large main cluster. The areaassociation is then preferably performed collectively to areas optimizedfor each subcluster.

A typical situation where clustering into several distinct locations islikely to occur may be when a cell comprises several distinct locationswith high probability for UEs to be present, separated by areas in whichUEs almost never enter. A couple of shopping malls separated bystock-room areas where non-authorized persons are prohibited can be oneexample. Another example is a number of heavily used freeways separatedby non-used areas.

According to the present invention a method for clustering positiondeterminations for providing position determination assisting data in acellular communications network is provided, allowing generation ofsubclusters. The method requires that a main cluster of points isavailable, which points are the results of high-precision positiondeterminations, preferably for a particular cell relation configuration.The main cluster of points can be obtained from an external party or canbe obtained by clustering, e.g. according to the above description. Themethod comprises separation of the main cluster into at least twosubclusters in such a way that the points in the subclusters have alocal density of points above a predetermined local density threshold.Preferably the method also comprises determination of a local density ofpoints for each point in the main cluster of points.

The present novel algorithms for splitting of a cluster of highprecision position measurement into multiple smaller clusters, enablesan enhancement of the accuracy of the AECID positioning method. TheAECID is allowed to create multiple polygons per tag. The finer divisionmakes it possible to optimize the corresponding areas better, whichmeans that the multiple polygons per tag taken together cover a smallertotal area than would a single polygon, corresponding to the originalcluster.

Post-processing algorithms are preferably included allowing fordetection and suppression of degenerated cases, and resulting clustersof insignificant size. The splitting the original cluster thus takesplace only in situations where the majority of the high precisionposition measurements are located in geographically distinct regions.This allows the complete algorithm to operate in autonomous mode.

Some examples where AECID clustering can be expected to allow splittingof a main cluster were presented above. Other examples are e.g. hillyterrain, in which cellular coverage can be expected to be located onslopes oriented towards the antenna of a specific cell, with reducedcoverage in valleys between slopes. This would affect the cluster viafailures in tagging high precision measurements according to proper cellrelation configurations. This may therefore result in a lack ofcorrectly tagged high precision position measurements in certainregions.

Terrain that prevents users from being located in certain areas, e.g.when cell coverage extends over a wide river, or several islands inarchipelagos, is another example where the present ideas would beparticularly advantageous. The properties of the terrain would affectthe cluster of high precision directly, since essentially no highprecision positioning attempts will be initiated in the areas whereusers cannot be located. Subclusters may in such a case exclude suchareas from being presented as possible AECID positions.

In certain cases, there are separate coverage regions of a cell in frontof and behind an antenna. Subclusters covering each region will thengive a better AECID positioning.

The actual effect of the splitting of main clusters in several smallersubclusters, with the same tag of cell relation configuration, is anexclusion of uninteresting parts of the regions of the initial un-splitcluster. The areas computed by AECID for the subclusters would then,summed up, cover a smaller area than an area computed from the original,un-split main cluster. The key point is that the reduced area results inan enhanced accuracy, when reported back over the service interface.

The purpose of the algorithm that is to be described is to attempt and,if possible, find a division of the original main cluster into a set ofsmaller subclusters that cover distinct regions of the original maincluster, at the same time as the subclusters contain a sufficiently highfraction of the points of the original main cluster.

In a presently preferred embodiment, the splitting into subclusters isbased on an initial selection of an original point from which thesubcluster gradually evolves. FIG. 5 illustrates a flow diagramillustrating steps of an embodiment of a method for clustering positiondeterminations for providing position determination assisting data in acellular communications network. The method begins in step 201. In step209 a main cluster of points is obtained. At least two subclusters areseparated out in step 220. The points of the subcluster have a localdensity of points above a predetermined local density threshold. In thepresent embodiment, step 220 comprises a number of part steps. In step221 a local density of points for each point in the main cluster ofpoints is determined. Such a determination can be performed in differentways. A typical procedure to use is to define a measure of the “local”area, e.g. a radius around the point and determine an average density ofpoints within such local area. One embodiment is given in Appendix B ina mathematical language.

Within step 220, in step 222, a point in the main cluster of points isselected to be included in a subcluster. That point has a local densityof points that is larger than the predetermined local density threshold.In step 230, points of the main cluster of points are included in thefirst subcluster. These included points have a local density of pointsthat is larger than the predetermined local density threshold. In oneembodiment described further below, the points to be included shouldalso have a distance to any other point included in the subcluster thatis smaller than a predetermined distance threshold. Details ofembodiments of step 230 are given further below.

In step 240, it is checked whether there are more subclusters to beformed. If that is the case, the procedure returns to step 222 again.The points selected to be included in a subcluster are excluded frombeing included in a subsequent formed subcluster. In other words, thepoint being selected in step 222 for a subsequent, n:th, subcluster,where n>1, has a local density of points that is larger than thepredetermined local density threshold and is not included in any of the(n−1) previous subclusters. Likewise, in step 230 for a subsequent,n:th, subcluster, points of the main cluster of points are included. Theincluded points have a local density of points that is larger than thepredetermined local density threshold and is not included in any of the(n−1) first subclusters. In one embodiment described further below, thepoints to be included should also have a distance to any point includedin the n:th subcluster that is smaller than the predetermined distancethreshold. The predetermined local density threshold and/or thepredetermined distance threshold are in one embodiment the same for allsubclusters. However, in other embodiments, the predetermined localdensity threshold and/or the predetermined distance threshold may differbetween different subclusters.

The criterion for creating more subclusters may differ between differentembodiments. One approach is to have a maximum number N of allowedsubclusters, whereby the steps 222 and 230 are repeated until Nsubclusters are formed. If no more subclusters can be formed using thecriteria of the predetermined local density threshold and thepredetermined distance threshold, there is an alternative to lower therequirements for obtaining the last subclusters. Remaining points of themain cluster not being included are typically discarded. Anotherapproach is to continue until no more subclusters can be formed usingthe criteria of the predetermined local density threshold and thepredetermined distance threshold. These two approaches can also becombined, i.e. forming subclusters according to the original criteria,but having a maximum allowed number of subclusters.

The procedure ends in step 298.

The inclusion of further points into a subcluster can be performed indifferent ways. One approach to include further points into a subclusteris illustrated in FIG. 6. The procedure starts in step 231. Here thepoint selected in step 222 (FIG. 5) is denoted as a first referencepoint. In step 232, a point of the main cluster of points that is notyet included in the subcluster is selected as a candidate point. In step233, it is checked if the candidate point has a local density of pointsthat is larger than the predetermined local density threshold and has adistance to the reference point that is smaller than the predetermineddistance threshold. If that is the case, the procedure continues to step234. Otherwise the procedure continues to step 235. In step 234, thecandidate point is included in the subcluster. In step 235, it isdetermined if there are any remaining points not yet tried as acandidate point. In such a case, the procedure returns to step 232. Inother words, the checking of candidates continues until no further pointremains, that is not included in the subcluster have a local density ofpoints that is larger than the predetermined local density threshold andhave a distance to the reference point that is smaller than thepredetermined distance threshold.

If all points have been tried as candidates, the procedure continues tostep 236, where it is determined whether all points included in thesubcluster has been utilized as reference point. If that is the case,the procedure continues to step 239, otherwise, if there are remainingpoints included in the subcluster that has not been utilized asreference point, the procedure continues to step 237. In step 237, a newreference point is selected among points in the subcluster that has notpreviously being selected as a reference point, and the procedure startsall over again with step 232. The procedure ends in step 239.

A mathematical description of another embodiment of a subclusterselection procedure is also given in Appendix C. This embodimentoperates on (x_(i) ^(p) y_(i) ^(p))^(T), i=1, . . . , N^(p), whichdenote the high precision measurements of the original cluster with tagp. The points are assumed to be represented in a local Cartesianearth-tangential co-ordinate system, i.e. not as WGS 84 latitudes andlongitudes.

The cluster splitting algorithm attempts to solve the problem withhighly heterogeneous areas by repeated subclustering attempts, appliedto the points of the original main cluster. A specific criterion is usedto stop each subclustering attempt. The local density of points isuseful as a stopping criterion. FIG. 7 illustrates a graph illustratingan original tagged main cluster 110 of high precision positionmeasurements, i.e. high precision position measurements having the samecell relation configuration.

In FIG. 7, it is obvious for anyone skilled in the art that at least 2,possibly 3 separate regions with a significantly higher local density ofpoints exist. These regions are all candidates for becoming separatesubclusters. The high local density regions are separated by regionswhere the local density of points is significantly lower than in thehigh local density regions. The algorithm of Appendix C exploits thisfact by starting a build-up of a subcluster in a selected high localdensity point. The algorithm then proceeds to add the geographicallynearest neighbour to the subcluster. The algorithm proceeds by repeatingthis stepping to the next nearest neighbour until the local density ofthe nearest neighbour point becomes too low. The algorithm then stepsback to one of the points already added to the subcluster, and initiatesa new search for nearest neighbours. This time nearest neighboursalready added to the subcluster are excluded. The algorithm proceeds byrepeating this search for nearest neighbours until all current pointsadded to the subcluster has been used as starting points for a nearestneighbour search. The algorithm stops when no more points are added tothe subcluster, or when all points of the original main cluster has beenadded to the subcluster.

It is clear that in order for the procedure to work efficiently, a localdensity of points need to be calculated, for each point of the originalcluster, preferably according to Appendix B. Obviously, the low localdensity regions between high local density regions play the roles as“stopping regions” when a subcluster is built up. The build-up procedureensures that a high number of different attempts are made to cross the“stopping regions”, thereby preventing the creation of unnecessarilysmall subclusters.

When the build-up of one subcluster has been finalized, the algorithmrepeats the attempt to create subclusters, using the points of theoriginal main cluster that has not yet been consumed by previous buildupof subclusters.

This approach is illustrated as a flow diagram in FIG. 8. The procedurestarts in step 251. Here the point selected in step 222 (FIG. 5) is thepoint not yet included in any subcluster having the highest localdensity of points and is denoted as a first start point. In step 252,the start point is selected as a first reference point. In step 253, theclosest neighbour point to the reference point that is not alreadyincluded in any subcluster is found. In step 254 it is checked if theclosest neighbour point fulfils the local density requirements. If so isthe case, the closest neighbour point is included in the subcluster instep 255 and selected as a new reference point in step 256. Theprocedure then returns to step 253 for repetition of the search for theclosest neighbour.

If the closest neighbour point in step 254 has a too low local densityof points. the procedure continues to step 257, where it is determinedif there are any points included in the subcluster that is not yet usedas a start point. If this is the case, one of these points are selectedin step 258 as a new start point and the procedure returns to step 252.Otherwise, the procedure ends in step 259.

In an alternative embodiment, the search for the nearest neighbour canalso be connected to a maximum distance. in analogy with the embodimentof FIG. 6.

Post-processing algorithms are as mentioned above preferably included incertain embodiments or for certain applications. The reasons can beunderstood by referring to FIG. 9. Here, the main cluster 110 of pointsof FIG. 7 has been split into four subclusters 101-104 according to theprinciples of Appendix C. The following observations can be made withrespect to FIG. 9. The subclusters 101 and 102 are correct. Thesubcluster 103 that surrounds the subcluster 101 is erroneous. However,it is easy to understand that such subclusters can result from “outerremnants” of high local density regions that are still dense enough toallow formation of a subcluster.

Very small subclusters, such as subcluster 104, with only a few pointsin it may also result, which obviously does not contribute in anyessential degree to the positioning accuracy. Furthermore, isolatedpoints of the original main cluster that are not contained in anysubcluster are also present. This occurs frequently when the maximumnumber of subclusters produced by the algorithm is specified. Suchisolated points are typically discarded.

In this case, there is hence a need for post-processing procedures thatsuppress subclusters that are too small and that suppress subclustersthat circumvent other correctly obtained subclusters, i.e. removingencircling subclusters, either by further splitting or by discarding thesubcluster. Details of embodiments of such tests are included inAppendix D. A procedure for suppressing too small subclusters ispreferable based on discarding subclusters having less than apredetermined number of included points. The procedure for removingencircling subclusters comprises in one embodiment the step of comparingan average of the local density of points with a ratio between a totalnumber of points included in the encircling subcluster and an areaspanned by the points in the encircling subcluster. The procedure forremoving encircling subclusters comprises in another embodiment the stepof comparing an average distance between points included in theencircling subcluster and a center of gravity of the points included inthe encircling subcluster with a minimum distance between any of thepoints included in the encircling subcluster and the center of gravityof the points included in the encircling subcluster.

As a final sanity check, it should also be checked that the total numberof points in subclusters that pass the above mentioned post-processingprocedures is sufficiently high, as compared to the number of points inthe original main cluster p. In case the sanity check reveals that thetotal number of points in subclusters is too low, the threshold valuesused in the algorithms may be revised.

In the example of FIG. 9, the following values were obtained for thenumber of points in each cluster, the values of the ratio (15) of theAppendix D and the final decision of the proposed algorithm:

TABLE 1 Numerical example of post-processing. Subcluster No of pointsMean/min Valid subcluster 1 956 108.0212 Yes 2 263 68.3836 Yes 3 1605.3286 No 4 3 1.8219 No

Above, clustering of the main clusters has been based mainly on activesets of base stations. However, the cell relation configuration cancomprise other additional properties. The selection criterion for theclustering can thus also be made on other parameters. The Radio AccessBearer (RAB) could e.g. be one selection parameter. The coverage fordifferent RABs can differ considerably, and the borders betweendifferent part areas can thereby change their position considerably. Forinstance, traffic transmitted by a 64 kbps link may have a completelydifferent coverage area than traffic transmitted by a 384 kbps link. Byalso clustering the measurements e.g. with respect to the used RAB, willenable an improved positioning.

The information about the RAB is a type of auxiliary information aboutcircumstances of signalling that makes the selection criterion more areaselective. In a general approach, other auxiliary information can alsobe utilised in an analogue manner. Similarly, there are also auxiliarymeasurements of signalling properties that can be performed and used asa part of the selection criterion. An example is e.g. auxiliary RTFmeasurements, which is discussed further below. The selection criterioncan be thought of as an augmentation of the cell relation configuration.

Now, returning back to the method for providing position determinationassisting data in a cellular communications network of FIG. 4. When a UEis going to be positioned, the procedure enters into the section 216 forposition determination. This section starts with a step 218. in which acell relation configuration for the UE to be positioned is determined.This is typically performed in an analogue manner as in step 204. Instep 221, the relation between a certain cell relation configuration andan area definition is used to provide an area in which the UE to bepositioned is situated with a certain confidence. This confidence levelcorresponds to the pre-determined fraction used during the areaoptimization. The procedure ends in step 299. The accuracy of thepositioning may in the best cases be enough for e.g. the North-AmericanE-911 emergency positioning requirements. However, positions achieved inthis manner should not be used to improve the area definitions accordingto the section 202.

The timing of the different steps can be somewhat differing in differentembodiments. For instance, the two sections 202 and 216 may beinterleaved with each other. The step of optimising the area 212 maythen be triggered by the step of determining the cell relationconfiguration 218. The optimising step 212 is then preferably performedjust for the cell relation configuration that was determined in step218, in order to save time. If the relations are determined in advance,the positioning can be performed with a shorter delay. The latterembodiment having the optimization triggered by the need for positioninginstead ensures that the latest available data always is utilized.

The position determined in step 221 can constitute the finalpositioning, or it can constitute assistance data for a refinedpositioning. Then, an extra step has to be included, where the positionas achieved from the relation of step 221 is utilised in a furtherpositioning method in order to refine the positioning further. Suchfurther positioning methods can e.g. be RTT positioning or A-GPSpositioning.

The time stamping can also be utilised in systems where the distributionof user equipments is likely to differ considerably between differenttimes. For instance, if an office complex and a residence area arecomprised close to each other, it is e.g. more likely to find the userequipments in the residence area during the nights. Such variations canbe dealt with by discarding high-precision positioning data having arecording time of the day, of the week or of the year, that isconsiderably different from the present time. In other words, theclustering can be performed by only selecting measurements fulfilling acertain additional criterion. The area definitions can thereby be madetime dependent.

The step of associating an area 212, preferably in an optimum manner, tothe clusters is one of the more important parts of the positioningprocedure. This area associating can be performed in many ways. but theexact implementation does not influence the main ideas of the presentinvention very much. However, some considerations may be taken. In FIG.10, a presently preferred embodiment of determining an area associatedwith a cluster of points (main cluster or subcluster) is described morein detail. In step 260, all the high-precision measurement points,n_(TOT), for the cluster in question are encompassed by an area border.n_(TOT) is subsequently used as the inputted number of high-precisionmeasurement points in the first iteration of the following step. In step262, it is checked if the ratio (n_(k)−n)/n_(TOT) is larger or equal toa predetermined fraction R, where n is the number of high-precisionmeasurement points that is intended to be removed during the nextiteration of the routine. If the ratio is large enough, the areareduction can proceed at least one step further, and the procedurecontinues to step 264. In step 264, the area is reduced according to acertain pre-determined action plan to exclude n of the inputtedhigh-precision measurement points, leaving n_(k)−n remaining points,which is set as the new inputted number of high-precision measurementpoints for the next iteration. Preferably, step 264 is performed in sucha way that the area is minimized or at least reduced. The processreturns to step 262 again, which is illustrated by the arrow 266. If theratio in step 262 becomes smaller than R, the process is interrupted,since one iteration more would cause the ration to fall below R, and thearea is subsequently used as the area associated with the cell relationconfiguration in question.

In case the cluster in question is a non-split main cluster, theprocedure of FIG. 10 is performed once. If the main cluster is splitinto subclusters, as described above, the procedure is preferablyperformed for each subcluster separately. The total area associated tothe original main cluster is then an assembly of the subareas determinedfor at least two of the subcluster and preferably for all non-discardedsubclusters.

In several systems, among these the WCDMA (Wideband Code DivisionMultiple Access) system, the preferred representation of thegeographical extension of the cell is given by a cell polygon format.The extension of a cell is described by 3-15 corners of a closed polygonwhich does not intersect itself. The format is two-dimensional and thecorners are determined as pairs of longitudes and latitudes in the WGS84geographical reference system. An example is illustrated in FIG. 11.There, an example of a cell polygon 89 with corners 90 is illustrated.The RBS (Radio Base Station) is typically located close to one of thecorners 90 of the cell polygon 89 said RBS serves. 3GPP systems providefor a messaging format for cell polygons. In the present invention, whensplitting into subclusters has been performed, an area corresponding toa specific cell relation configuration comprises more than one polygon.

Furthermore, when the present invention is used as cell-ID positioningmethod, a number of re-calculated polygons, rather than pre-calculatedpolygons, that corresponds to the specific identity of the cell isreported over RANAP or Iupc (a logical interface between a RNC and a SASwithin the UTRAN).

If the present invention is used as enhanced cell identity positioning,making use of soft(er) handover active sets or detectable cell sets, asimilar reporting can take place. In case there are re-calculatedpolygons stored for the determined cell relation configuration, then there-calculated polygons are selected and reported over RANAP or Iupc.Again, the invention fits directly into the existing positioninginterfaces.

The area definition data should be organized so that it can beefficiently addressed using cell relation configuration information. Inthis way, fallback areas covering replacement regions, can be foundwhenever areas for certain regions have not been computed. Note thatthis situation may occur e.g. because of insufficient measurementstatistics.

For instance. in case no polygon is computed for the specific cellrelation configuration, then the hierarchical structure of the storedcell relations and area definitions is exploited in some way. Onealternative is to disregard the last cell identity of the cell relationconfiguration and look for the re-calculated polygon for the so reducedcell relation configuration. In case there is a re-calculated polygonfor this reduced cell relation configuration, then this polygon isreported over RANAP or Iupc. In case there is still no polygon computedthen the second last cell identity of the cell relation configuration isremoved and the procedure repeated. This procedure can continue up totop level. where the cell relation configuration corresponds to theserving cell. In case there would still not be a re-calculated polygon,the pre-calculated polygon can be used. It should be noted that thereare many alternative strategies that are possible here.

Position determination assisting data obtained by the proceduresdescribed above may also be provided for positioning purposes as storedat a computer readable medium.

FIG. 12 is a block diagram of an embodiment of a positioning node 45 andrelated functionality according to the present invention. In the presentembodiment, which is assumed to be comprised in a WCDMA system, suchfunctionality is preferably comprised in the RNC 40. Another possibilityis to implement the invention in the SAS node (e.g. an Ericsson SMLC) onthe other side of the Iupc interface 47. Still another possibility is tolog measurements and perform the algorithms in OSS-RC or even acompletely external node. New interfaces and/or information elements inexisting interfaces allowing for exchange of detected cell sets andmeasured high-precision position determination results may then benecessary.

In the case the position determination assisting data, i.e. therelations between the cell relation configurations and the associatedareas are produced in an external node, the information has to beprovided to a positioning node in order to assist in positiondetermination procedures. The position determination assisting data canthen preferably be stored at a computer readable medium, and supplied tothe positioning node in a suitable manner, e.g. by downloading thecontent over a communication link or simply by providing a data memorydevice having the data stored therein,

The RNC 40 communicates with UEs, transparently via RBSs, using the RRCinterface 37. In the present context, at least two information types areof interest: positioning measurements 38, in particular high-precisionpositioning measurements, and neighbouring cell signal measurements 39,e.g. handover measurements. The neighbouring cell signal measurements 39are provided to cell relation configuration determining section 41,determining the cell relation configuration. In a particular embodiment,the cell relation configuration determining section 41 can be based on aprior-art active set functionality. The determined cell relationconfiguration of a particular user equipment is provided to a clusteringsection 42.

The positioning measurements 38 are provided to the positioning node 45.The high-precision positioning measurements are provided to ahigh-precision positioning section 46, which e.g. can comprise UTDOA orA-GPS based positioning. Other positioning measurements, e.g. cell ID orKIT positioning measurements are in the present embodiment provided to amedium-precision positioning section 48. The outcome of the analysis ofthe high-precision positioning measurements, i.e. high-precisionpositions are provided to the clustering section 42, where thehigh-precision position is associated with a corresponding cell relationconfiguration. The measurements are clustered depending on the cellrelation configuration and in particular embodiments also on otherselection criteria such that auxiliary information and/or auxiliarymeasurements, in particular recording time, utilised RAB and/or RTTmeasurements. RTT measurements could then e.g. be provided by themedium-precision positioning section 48 as indicated by the broken arrow53. Auxiliary information, such as time or utilised RAB, and otherauxiliary measurements can be provided by an auxiliary informationsection 54. This auxiliary information section 54 can be arranged toprovide the information internally in the node and/or be arranged toachieve the information from outside.

In case a main cluster would benefit from being split into subclusters,the clustering section 42 is arranged for performing the proceduresdiscussed above. The clusters (one or several) of positions for acertain cell relation configuration and in some embodiments selectedwithin a specific time interval or using a specific RAB are provided toan algorithmic block 43. In the algorithmic block 43, area definitionsare calculated. One important objective of the present invention, tocompute an area that describes each cluster of measurements, at aspecified confidence level, is performed in the algorithmic block 43. Inthe WCDMA case, the preferred area definition is a polygon defined by 3to 15 corner coordinates. In case of split subclusters, the areadefinition becomes a group of polygons, preferably having maximum 15corner coordinates together. In a particular embodiment, the algorithmicblock 43 provides polygons such that the probability that a givenfraction of high-precision measurements of a cluster are located in theinterior of the polygon. This algorithmic block 43 preferably performsrepeated re-calculations of polygons, for all measurement clusters witha sufficient number of recent enough high-precision measurements. Thearea definitions are provided to an area storage 44, where polygonsrepresenting a hierarchically organized set of cell relationconfigurations are stored. The stored polygons are then used bypositioning algorithms of the system. The data structure of the storedpolygons preferably contains a list of pointers covering each relevantcell relation configuration. Each such pointer points to a corresponding3-15 corner polygon, or a group of corresponding polygons, computedrepeatedly as described above. The data structure preferably alsocontains a time tag for each polygon or group of polygons that definesthe time when the polygon or group of polygons were computed.

When a position determination according to the principles of the presentinvention is requested, a cell relation configuration is determined inthe cell relation configuration determining section 41 as usual. Theresult is forwarded to a control section 49 in the positioning node 45.When a positioning request 51 is received. e.g. a so-called LocationReporting Control message over the RANAP interface 47, the controlsection 49 may, based on quality of service parameters and UEcapability, request a position determination by retrieving an areadefinition from the area storage 44, which corresponds to the presentcell relation configuration of the UE. The achieved area definition,preferably a polygon definition or a definition of a group of polygonsis included in a positioning reporting message 52, which typically issent back over the RANAP interface 47 using e.g. a so-called LocationReport message. As in the phase of creating the position determinationassisting data, auxiliary information, such as time or utilised RAB, andother auxiliary measurements can also be used to refine the selection ofthe area definition. Such data is achieved by the auxiliary informationsection 54.

If the area definitions are to be used together with any additionalpositioning method, the retrieved area from the area storage 44 isprovided to the high-precision positioning section 46 or themedium-precision positioning section 48, depending on the method to beused. The final determined position is then provided to the controlsection 49 for further reporting.

Most functionalities of the cell relation configuration determiningsection 41, the high-precision positioning section 46, themedium-precision positioning section 48 and the control section 49 aretypically available in prior art systems. However, connections creatingrelations between the cell relation configuration determining section 41on one side and the high-precision positioning section 46, themedium-precision positioning section 48 and the control section 49 onthe other side are previously unknown. Furthermore, the clusteringsection 42, the algorithmic block 43, the area storage 44 as well asconnections thereto are entirely novel. So is also functionality in thecell relation configuration determining section 41, the high-precisionpositioning section 46, the medium-precision positioning section 48 andthe control section 49 needed for communicating with these novelfunctionalities.

A preferred embodiment of the invention can be summarized as follows.Algorithms for splitting of a main cluster of high precision positionmeasurements into multiple subclusters that, taken together, cover anarea that is smaller than the original cluster, are provided. Therebythe accuracy of the AECID positioning algorithm can be enhanced.

The embodiments described above are to be understood as a fewillustrative examples of the present invention. It will be understood bythose skilled in the art that various modifications, combinations andchanges may be made to the embodiments without departing from the scopeof the present invention. In particular, different part solutions in thedifferent embodiments can be combined in other configurations, wheretechnically possible. The scope of the present invention is, however,defined by the appended claims.

Appendix A

Clustering

In this particular embodiment, it is assumed that the cell relationconfiguration is based on the active list of cells, i.e. cells active insoft handover. Corresponding modelling is possible also for othercluster selection rules.

The high-precision position measurements are typically obtainedexpressed in the WGS 84 geographical reference system. The measurementsthat are available at time t are denoted(lat_(j)(t _(j)) long_(j)(t _(j)))^(T), j=1, . . . , N(t).  (1)where lat_(j)(t_(j)) and long_(j)(t_(j)) denote the measured latitudeand longitude, respectively, at the time t_(j). N(t) denotes the totalnumber of available measurements at time t. ( )^(T) denotesmatrix/vector transpose.

At the same time t_(j) (to within some reasonable accuracy in time), thecell relation configuration is sampled for cell identities. The resultis the row vector (or pointer)Configuration (t _(j))=(cID₁(t _(j)) cID₂(t _(j)) . . . cID_(N(t) _(j)₎(t _(j))),  (2)where cID₁(t_(j)) is the cell identity of the l:th strongest cell ine.g. softer handover, for the UE for which high-precision positioningwas performed at time t_(j). N(t_(j)) is the number of cells in the cellrelation configuration at time t_(j).

An arbitrary possible pointer (or equivalently tag) used for clusteringof measurements, defined according to (2), is now denoted byPointer_(k)=(Index₁(k) . . . Index_(N(k))(k)), k=1, . . . , K  (3)where Index_(l)(k) is the l:th component of the (fix) pointer k, N(k) isthe dimension of the pointer k and K is the number of counters. Thecorresponding list of high-precision position measurements is denoted byList_(k). At time t:

$\begin{matrix}{{{{List}_{k}(t)} = \begin{pmatrix}{{lat}_{k,1}\left( t_{k,1} \right)} & {{lat}_{k,2}\left( t_{k,2} \right)} & \ldots & {{lat}_{k,{M{({k,t})}}}\left( t_{k,{M{({k,t})}}} \right)} \\{{long}_{k,1}\left( t_{k,1} \right)} & {{long}_{k,2}\left( t_{k,2} \right)} & \ldots & {{long}_{k,{M{({k,t})}}}\left( t_{k,{M{({k,t})}}} \right)} \\t_{k,1} & t_{k,2} & \ldots & t_{k,{M{({k,t})}}}\end{pmatrix}},} & (4)\end{matrix}$where M(k,t) denotes the number of high-precision measurements of list kat time t. As stated above, measurements that are older than apre-specified threshold are discarded from each list. The maximum sizeof a list can also be pre-specified, in which case the oldestmeasurement is discarded irrespective of its age when a new measurementarrives.

When a new high-precision measurement and corresponding cell relationconfiguration is obtained at time t_(N(t)+1) the clustering algorithmoperates as follows:

For k = 1 to K If Pointer_(k) = Configuration(t_(N(k)+1))${{List}_{k}\left( t_{{N{(k)}} + 1} \right)} = \left( {{{List}_{k}(t)}\begin{pmatrix}{{lat}_{{N{(t)}} + 1}\left( t_{{N{(t)}} + 1} \right)} \\{{long}_{{N{(t)}} + 1}\left( t_{{N{(t)}} + 1} \right)} \\t_{{N{(t)}} + 1}\end{pmatrix}} \right)$ end else do nothing end end

Appendix B

Calculation of Local Densities of Points

This step introduces the algorithmic parameter λ^(p)—the fraction of thetotal extension of the main cluster p, that is used for definition ofthe limiting circle, within which the local density of points is to becomputed, for each point (x_(i) ^(p) y_(i) ^(p))^(T), i=1, . . . ,N^(p). Note that this fraction may be different for different clustersp.

The total extension of the cluster p follows as

$\begin{matrix}{R^{p} = {{\max\limits_{i,j}{{\begin{pmatrix}x_{i}^{p} & y_{i}^{p}\end{pmatrix} - \begin{pmatrix}x_{j}^{p} & y_{j}^{p}\end{pmatrix}}}_{2}} = {\max\limits_{i,j}{\sqrt{\left( {x_{i}^{p} - x_{j}^{p}} \right)^{2} + \left( {y_{i}^{p} - y_{j}^{p}} \right)^{2}}.}}}} & (5)\end{matrix}$

The radius of the circle, used for evaluation of the local density ofpoints becomesr ^(p)=λ^(p) R ^(p).  (6)

The local density of points, for each point in the cluster p follows as

$\begin{matrix}{\rho_{i}^{p} = {\frac{1}{{\pi\left( r^{p} \right)}^{2}}{\sum\limits_{j{{{({(\begin{matrix}x_{i}^{p} & y_{j}^{p}\end{matrix})}} \leq r^{p}}}}1.}}} & (7)\end{matrix}$

A typical value of λ^(p) may be 0.02-0.10.

Appendix C

Selection of Stopping Density Threshold

First, a threshold ρ^(p,threshold), below which the stepping to a nextnearest neighbour is stopped has to be determined. A specific problem isthen that the number of points N^(p) in a cluster, as well as themaximal extension of the clusters may vary significantly. For thisreason, it is not possible or at least not preferable to set a globallyvalid value of the threshold, it needs to be set taking the points ofeach cluster in consideration.

Instead, in “stopping regions” ρ_(i) ^(p) should be close to the lowestobserved values of the original cluster. Due to the low local densityρ_(i) ^(p) in “stopping regions”, the vast majority of points in theoriginal cluster has higher densities than the points in stoppingregions. Using these two observations, the following approach forthreshold setting is preferably adopted. The densities ρ_(i) ^(p), i=1,. . . . , N^(P) are first sorted in descending order, resulting in thedecreasing sequence of densities:

$\begin{matrix}{\left\{ \rho_{i{(j)}}^{p} \right\}_{i = 1}^{N_{p}}.} & (8)\end{matrix}$

The mapping i(j) reflects the sorting of densities in descending order.A relative percentile type parameter γ^(p), that may depend on thecluster p, is then introduced in order to point out the index of (8)that is such that:i(j ^(•))=[γ^(p) N ^(p)],  (9)where [ ] denotes the integer of the argument. Typically γ^(p) may beselected in the interval 0.8-0.95, meaning that 20%-5% of the pointshave lower local density values than the one corresponding to theselected γ^(p). The absolute stopping threshold, is then selected as:

$\begin{matrix}{\rho^{p,{threshold}} = {\rho_{i{(j^{\prime})}}^{p}.}} & (10)\end{matrix}$

The approach has the advantage that it is normalized, or adaptive, withrespect to both the number of points in each original cluster and thegeographical extension of each geographical cluster. This is a preferredprerequisite for autonomous operation in a system with thousands oforiginal AECID clusters, each cluster corresponding to a specific cell,or sub-region of a cell

Split Cluster Formation

In order to describe the algorithm for formation of split clusters, i.e.forming of subclusters, the following notation and variables will beused below:

-   SC^(p)—The number of the subcluster.-   N^(p,SC)—The maximum number of subclusters.-   c^(p)(i), i=1, . . . , N^(p)—The number of the subcluster, to which    the point (x_(i) ^(p) y_(i) ^(p))^(T) belongs. c^(p)(i)=0 means that    the point does not yet belong to a subcluster.-   usedForStart(i), i=1, . . . , N^(p)—A Boolean that indicates which    of the points that have been used as starting points for a new    “closest neighbour stepping” search.

The algorithm can now be written in pseudo-code as follows, for oneoriginal cluster p. “%” signs are used for comments:

SC = 1 %-start with first subcluster ClusterSplittingNotReady = ′True′While (clusterSplittingNotReady) usedForStart(i) = 0, i = 1, . . . ,N^(p) %-init not used start point indicator$i^{\max} = {\underset{i}{\arg\mspace{11mu}\max}\left\{ {\rho_{i}^{p}❘{{c(i)} \equiv 0}} \right\}}$%-find the index of the un-used point with maximum local densityc^(p)(i^(max)) =SC %-mark starting point with subcluster numberClosestNeighbourStepping =′True′ while (closestNeighbourStepping){i^(SteppingStart)(j)}_(j = 1)^(M) = {c^(p)(i) ≡ SC  AND  usedForStart(i) ≡ 0}%-find possible starting points for closest neighbour stepping if (M >0) %-if there is at least one-pick the first one and startusedForStart(i^(SteppingStart)(1)) = 1 %-mark it(x_(i^(SteppingStart(1)))^(p) y_(i^(Steppingstarty(1)))^(p))^(T)%-Initialize with starting point endOfThisStepping =′False′ while (Not endOfThisStepping) %-One neighbour stepping to local density threshold$i^{Closest} = {\underset{i}{\arg\mspace{11mu}\min}\left\{ {{{{{thisPoint} - \left( {x_{p}^{i}\mspace{14mu} y_{p}^{i}} \right)}}❘{i \neq i^{SteppingStart}}},{{c^{p}(i)} \equiv 0}} \right\}}$%-find index of closest neighbour point if(ρ_(i^(Closest))^(p) > ρ^(p.threshold)) %-local density large enough toadd point to current subcluster? c^(p)(i^(Closest)) = SC thisPoint =(x_(i) _(Closest) ^(p) y_(i) _(Closest) ^(p))^(T) else %-Local densityto small-try another round of stepping endOfThisStepping = 1 end end%-To while loop{i^(SteppingStart)(j)}_(j = 1)^(M) = {c^(p)(i) ≡ SC  AND  usedForStart(i) ≡ 0}%-try to find new starting points for closest neighbour stepping end%-To if M>0 clause if (M = 0) %-if there is no starting point-thissubcluster is done closestNeighbourStepping =′False′ end end %-Remainsto check if all sublusters are done? if (c^(p)(i) > 0, ∀i  OR  SC >N^(p.SC)) clusterSplittingNotReady =′False′ end if (SC < N^(p.SC)) SC =SC + 1 end end

Appendix D

Test of Small Subclusters

In order to formalize the test for small subclusters it is noted that itcan be formulated as:N ^(p)(SC)>υ^(p) N ^(p).  (11)

Here N^(P)(SC) is the number of points in the subcluster with number SC.N^(P) denotes the total number of points in the original main cluster,and υ^(p) is a threshold. A typical value for υ^(p) may be 0.05.Subclusters that do not fulfill (11) are discarded.

Test of Small Encircling Subclusters I

One proposed embodiment is based on the observation that, contrary to acorrect split cluster, all points in a circumventing or encirclingsubcluster has a large minimal distance to a centre of gravity of thesubcluster. A measure using the quotient between the mean distance tothe centre of gravity and the minimum distance to the centre of gravityshould be successful. In order to formalize the test the centre ofgravity of all subclusters are computed as:

$\begin{matrix}{{\begin{pmatrix}{x_{CG}^{p}({SC})} & {y_{CG}^{p}({SC})}\end{pmatrix}^{T} = {\frac{1}{N^{p}({SC})}{\sum\limits_{\underset{{c{(j)}} = {SC}}{j = 1}}^{N^{p}}\begin{pmatrix}x_{j}^{p} & y_{j}^{p}\end{pmatrix}^{T}}}},{{SC} = 1},\ldots\mspace{14mu},{N^{p,{SC}}.}} & (12)\end{matrix}$

The mean distance and the minimum distance between points in thesubcluster and the center of gravity of the subcluster, are thencomputed:

$\begin{matrix}{{{r_{mean}^{p}({SC})} = {\frac{1}{N^{p}({SC})}{\sum\limits_{\underset{{c{(j)}} = {SC}}{j = 1}}^{N^{p}}{{\begin{pmatrix}x_{j}^{p} & y_{j}^{p}\end{pmatrix} - \begin{pmatrix}{x_{CG}^{p}({CG})} & {y_{CG}^{p}({SC})}\end{pmatrix}}}_{2}}}},} & (13) \\{{{r_{\min}^{p}({SC})} = {\min\limits_{i}{{{\begin{pmatrix}x_{i}^{p} & y_{i}^{p}\end{pmatrix} - \begin{pmatrix}x_{CG}^{p} & y_{CG}^{p}\end{pmatrix}}}.}}}\mspace{115mu}} & (14)\end{matrix}$

The test quantity:

$\begin{matrix}{{{\xi({SC})} = \frac{r_{mean}({SC})}{r_{m\; i\; n}({SC})}},{{SC} = 1},\ldots\mspace{14mu},N^{p,{SC}}} & (15)\end{matrix}$is then computed. Since the first subcluster formed cannot normallybecome a circumventing subcluster, it follows that the remainingsubclusters can be compared to the first subcluster. Subclusters arediscarded whenever:

$\begin{matrix}{{\frac{\xi(1)}{\xi({SC})} > \tau^{p}},} & (16)\end{matrix}$where the threshold τ^(p) is typically 5-10.Test of Small Encircling Subclusters II

Another proposed embodiment is based on the observation that, contraryto a correctly split cluster. an average of the local density of pointsdiffers considerably compared with a ratio between a total number ofpoints included in the subcluster and an area spanned by the points inthe subcluster. A measure using the quotient between these twoquantities should be successful. The average of the local density ofpoints of subcluster SC is given by:

$\begin{matrix}{{{{\hat{\rho}}^{p}({SC})} = {\frac{1}{N^{p}({SC})}{\sum\limits_{\underset{{c{(j)}} = {SC}}{j = 1}}^{N^{p}}\rho_{j}^{p}}}},{{SC} = 1},\ldots\mspace{14mu},{N^{p,{SC}}.}} & (17)\end{matrix}$

The area spanned by the points in the subcluster may be more intricateto calculate. However, the square of the maximum distance between anytwo points in the subcluster will give an estimate of the area. Such aquantity is obtained by:A ^(P)(SC)=(R ^(p)(SC))², SC=1, . . . , N^(p,SC).  (18)where R^(p) is given by (5). Subclusters are discarded whenever:

$\begin{matrix}{{\frac{{\hat{\rho}}^{p}({SC})}{{N^{p}({SC})}/{A^{p}({SC})}} > \tau^{p}},} & (19)\end{matrix}$where the threshold τ^(p) is typically 2-5.

1. Method for clustering position determinations for providing positiondetermination assisting data in a cellular communications network, themethod comprising: obtaining a main cluster of points being results ofhigh-precision position determinations for different positions of userequipment terminals in communication with the cellular communicationsnetwork; and separating out the points that are the results of thehigh-precision position determinations into at least two subclusters ofthe points, the respective points of each of the subclusters having alocal positional density of points above a predetermined localpositional density threshold, where said separating out comprises,determining a local positional density of points for each point in saidmain cluster of points, selecting a point in said main cluster of pointshaving a local positional density of points larger than saidpredetermined local positional density threshold to be included in afirst subcluster, and including points of said main cluster of points insaid first subcluster, said points included in said first subclusterhaving a local positional density of points that is larger than saidpredetermined local positional density threshold.
 2. Method according toclaim 1, where said points included in said first subcluster having adistance to any other point included in said first subcluster that issmaller than a predetermined distance threshold.
 3. Method according toclaim 2, where said selecting a point in the main cluster of pointscomprises selecting said point as a first reference point; and saidincluding points comprises: selecting a candidate point of said maincluster of points not being included in said first subcluster; checkingif said candidate point having a local positional density of points thatis larger than said predetermined local positional density threshold andhaving a distance to said reference point that is smaller than saidpredetermined distance threshold; including said candidate point in saidfirst subcluster if a local positional density of points of saidcandidate point is larger than said predetermined local positionaldensity threshold and if said candidate point having a distance to saidreference point that is smaller than said predetermined distancethreshold; and repeating the selecting a candidate point, the checking,and the including the candidate point until no further points not beingincluded in said first subcluster having a local positional density ofpoints that is larger than said predetermined local positional densitythreshold and having a distance to said reference point that is smallerthan said predetermined distance threshold remain.
 4. Method accordingto claim 3, where said including points comprises: selecting a newreference point among points in said first subcluster not previouslybeing selected as a reference point; repeating the selecting a candidatepoint, the checking, the including the candidate point, and theselecting a new reference point until all points in said firstsubcluster have been utilized as a reference point.
 5. Method accordingto claim 1, where said selecting a point in the main cluster of pointscomprises selecting the point in said main cluster of points having ahighest local positional density of points to be included in a firstsubcluster and being selected as a start point; and said includingpoints comprises: selecting said start point as a reference point;finding a closest neighbour point of said main cluster of points notbeing included in said first subcluster and not being utilized as startpoint; checking if said closest neighbour point having a localpositional density of points that is larger than said predeterminedlocal positional density threshold; including said closest neighbourpoint in said first subcluster and selecting said closest neighbourpoint as new reference point if a local positional density of points ofsaid candidate point is larger than said predetermined local positionaldensity threshold; and repeating the finding a closest neighbour point,the checking if the closest neighbour point, and the including theclosest neighbour point until said closest neighbour point having alocal positional density of points that is smaller than saidpredetermined local positional density threshold; selecting a pointincluded in said first subcluster not previously being utilized as startpoint as a new start point; and repeating the selecting the startingpoint, the finding a closest neighbour point; the checking if theclosest neighbour point; the including the closest neighbour point, andthe including the closest neighbour point; and the selecting the pointincluded in the first subcluster until all points included in said firstsubcluster have been utilized as start points.
 6. Method according toclaim 1, where selecting a point comprises selecting a first point, andwhere said separating out comprises: selecting a second point in saidmain cluster of points having a local positional density of pointslarger than said predetermined local positional density threshold andnot being included in any of the first subcluster to be included in asecond subcluster; and including points of said main cluster of pointsin said second subcluster, said points included in said secondsubcluster having a local positional density of points that is largerthan said predetermined local positional density threshold not beingincluded in the first subcluster.
 7. Method according to claim 6, wheresaid separating out further comprises: selecting a third point in saidmain cluster of points having a local positional density of pointslarger than said predetermined local positional density threshold andnot being included in any of the first and second subclusters to beincluded in a third subcluster; and including points of said maincluster of points in said third subcluster, said points included in saidthird subcluster having a local positional density of points that islarger than said predetermined local positional density threshold notbeing included in the first subcluster.
 8. Method according to claim 1,further comprising: discarding subclusters having less than apredetermined number of included points.
 9. Method according to claim 1,further comprising: removing encircling subclusters.
 10. Methodaccording to claim 9, where the removing encircling subclusterscomprises discarding of said encircling subclusters.
 11. Methodaccording to claim 9, where the removing encircling subclusterscomprises splitting of said encircling subclusters into furthersubclusters.
 12. Method according to claim 9, where the removingencircling subclusters comprises comparing an average of said localpositional density of points with a ratio between a total number ofpoints included in said encircling subcluster and an area spanned bysaid points in said encircling subcluster.
 13. Method according to claim9, where the removing encircling subclusters comprises comparing anaverage distance between points included in said encircling subclusterand a center of gravity of said points included in said encirclingsubcluster with a minimum distance between any of said points includedin said encircling subcluster and said center of gravity of said pointsincluded in said encircling subcluster.
 14. Method according to claim 1wherein the high-precision position determinations are configured toprovide positioning accuracy of 100 meters at 67% and positioningaccuracy of 300 meters at 95%.
 15. Method according to claim 1 whereinthe high-precision position determinations are made using GlobalPositioning System (GPS) positioning and/or Uplink Time Difference OfArrival (UTDOA) positioning.
 16. Method according to claim 1 whereinobtaining the main cluster comprises obtaining the main cluster at aclustering section of the communications network and wherein separatingout the points comprises separating out the points at the clusteringsection of the communications network.
 17. Method according to claim 1further comprising: providing a position determination for a particularuser equipment terminal using at least one of the subclusters. 18.Method for providing position determination assisting data in a cellularcommunications network, the method comprising: establishing a cellrelation configuration for user equipment terminals, said cell relationconfiguration comprising at least cell identities of cells, in whichsignals to/from said user equipment terminals fulfill at least aspecific radio condition criterion when received; performinghigh-precision position determinations for different positions of saiduser equipment terminals; repeating said establishing a cell relationconfiguration and said performing a high-precision positiondeterminations a plurality of times; clustering points being results ofsaid high-precision position determinations belonging to the same cellrelation configuration in separate main clusters of points; splitting atleast one of said separate main clusters of points into a plurality ofsubclusters; associating an area definition with at least one of saidmain clusters of points; and creating position determination assistingdata comprising a relation between said cell relation configurations andsaid associated area definitions; where said splitting comprises,determining a local positional density of points for each point in saidat least one of said main clusters of points, selecting a point in saidat least one of said main clusters of points having a local positionaldensity of points larger than a predetermined local positional densitythreshold to be included in a first subcluster, and including points ofsaid at least one of said main clusters of points in said firstsubcluster, said points included in said first subcluster having a localpositional density of points that is larger than said predeterminedlocal positional density threshold.
 19. Method according to claim 18,where the associating an area definition with at least one of said mainclusters of points comprises: associating a subarea definition with atleast two of said subclusters of at least one of said main clusters ofpoints; and defining said area definition as an aggregate of saidsubarea definitions.
 20. Method according to claim 18 wherein thehigh-precision position determinations are made using Global PositioningSystem (GPS) positioning and/or Uplink Time Difference Of Arrival(UTDOA) positioning.
 21. Arrangement for providing positiondetermination assisting data in a cellular communications network, thearrangement comprising: means for establishing a cell relationconfiguration for user equipment terminals, said cell relationconfiguration comprising at least cell identities of cells, in whichsignals to/from said user equipment terminals fulfill at least aspecific radio condition criterion when received; means for performinghigh-precision position determinations for different positions of saiduser equipment terminals in communication with the cellularcommunications network; means for clustering results of saidhigh-precision position determinations belonging to the same cellrelation configuration in separate main clusters of points, said meansfor clustering results being further arranged for separating out, fromat least one of said separate main clusters of points, at least twosubclusters of the points, the respective points of each of thesubclusters having a local positional density of points above apredetermined local positional density threshold; and means forassociating an area definition with at least two of said subclusters ofsaid main clusters of points and creating position determinationassisting data comprising a relation between said cell relationconfigurations and said associated area definitions; said means forclustering results being further arranged for, determining a localpositional density of points for each point in said main cluster ofpoints, selecting a point in said main cluster of points having a localpositional density of points larger than said predetermined localpositional density threshold to be included in a first subcluster, andincluding points of said main cluster of points in said firstsubcluster, said included points having a local positional density ofpoints that is larger than said predetermined local positional densitythreshold.
 22. Arrangement according to claim 21 wherein thehigh-precision position determinations are made using Global PositioningSystem (GPS) positioning and/or Uplink Time Difference Of Arrival(UTDOA) positioning.
 23. Node of a cellular communications network,comprising: an arrangement comprising: means for establishing a cellrelation configuration for user equipment terminals, said cell relationconfiguration comprising at least cell identities of cells, in whichsignals to/from said user equipment terminals fulfill at least aspecific radio condition criterion when received; means for performinghigh-precision position determinations for different positions of saiduser equipment terminals in communication with the cellularcommunications network; means for clustering results of saidhigh-precision position determinations belonging to the same cellrelation configuration in separate main clusters of points, said meansfor clustering results being further arranged for separating out, fromat least one of said separate main clusters of points, at least twosubclusters of the points, the respective points of each of thesubclusters having a local positional density of points above apredetermined local positional density threshold; and means forassociating an area definition with at least two of said subclusters ofsaid main clusters of points and creating position determinationassisting data comprising a relation between said cell relationconfigurations and said associated area definitions; where said meansfor clustering results being further arranged for, determining a localpositional density of points for each point in said main cluster ofpoints, selecting a point in said main cluster of points having a localpositional density of points larger than said predetermined localpositional density threshold to be included in a first subcluster, andincluding points of said main cluster of points in said firstsubcluster, said included points having a local positional density ofpoints that is larger than said predetermined local positional densitythreshold.
 24. Cellular communications network, comprising: anarrangement comprising: means for establishing a cell relationconfiguration for user equipment terminals, said cell relationconfiguration comprising at least cell identities of cells, in whichsignals to/from said user equipment terminals fulfill at least aspecific radio condition criterion when received; means for performinghigh-precision position determinations for different positions of saiduser equipment terminals in communication with said cellularcommunications network; means for clustering results of saidhigh-precision position determinations belonging to the same cellrelation configuration in separate main clusters of points, said meansfor clustering results being further arranged for separating out, fromat least one of said separate main clusters of points, at least twosubclusters of the points, the respective points of each of thesubclusters having a local positional density of points above apredetermined local positional density threshold; and means forassociating an area definition with at least two of said subclusters ofsaid main clusters of points and creating position determinationassisting data comprising a relation between said cell relationconfigurations and said associated area definitions; where said meansfor clustering results being further arranged for, determining a localpositional density of points for each point in said main cluster ofpoints, selecting a point in said main cluster of points having a localpositional density of points larger than said predetermined localpositional density threshold to be included in a first subcluster, andincluding points of said main cluster of points in said firstsubcluster, said included points having a local positional density ofpoints that is larger than said predetermined local positional densitythreshold.
 25. Non-transitory computer readable medium that storesinstructions that, when executed by a processor, cause the processor toperform a method for clustering position determinations for providingposition determination assisting data in a cellular communicationsnetwork, the method comprising: obtaining a main cluster of points beingresults of high-precision position determinations for differentpositions of user equipment terminals in communication with the cellularcommunications network; and separating out the points that are theresults of the high-precision position determinations into at least twosubclusters of the points, the respective points of each of thesubclusters having a local positional density of points above apredetermined local positional density threshold, where said separatingout comprises, determining a local positional density of points for eachpoint in said main cluster of points, selecting a point in said maincluster of points having a local positional density of points largerthan said predetermined local positional density threshold to beincluded in a first subcluster, and including points of said maincluster of points in said first subcluster, said points included in saidfirst subcluster having a local positional density of points that islarger than said predetermined local positional density threshold. 26.Computer readable medium according to claim 25 wherein the computerreadable medium comprises a data memory device.